NaCl in Sputum
Rebecca Luise Knoll
last edit 13 September 2024
1 load packages and define colors
pacman::p_load(rmarkdown, tidyverse, phyloseq, magrittr, janitor, microbiome, knitr, lubridate, naniar, readxl, ggplot2, ggpubr,rstatix, metadeconfoundR, microViz)
id_palette <- c("11"="#4E79A7FF", "13"="#A0CBE8FF", "15"="#F28E2BFF", "16"="#FFBE7DFF",
"17"="#59A14FFF", "24"="#8CD17DFF", "25"="#B6992DFF", "26"="#F1CE63FF",
"27"="#499894FF", "29"="#86BCB6FF", "32"="#E15759FF", "33"="#FF9D9AFF",
"35"="#79706EFF", "5"="#BAB0ACFF", "6"="#D37295FF", "9"="#FABFD2FF",
"21"="#B07AA1FF", "31"="#D4A6C8FF", "8"="black")2 read data
df <- read_excel("/Users/rebecca/Documents/Forschung/IMMProveCF/2024_09_03_Mohr test Nacl calculations (Nacho).xlsx", sheet = "Results")## New names:
## • `` -> `...6`
## • `` -> `...10`
## • `` -> `...14`
## • `` -> `...15`
## • `` -> `...16`
## • `` -> `...17`
## • `` -> `...18`
## • `` -> `...19`
## • `` -> `...20`
## • `` -> `...21`
## • `` -> `...22`
## • `` -> `...23`
## • `` -> `...24`
## • `` -> `...25`
## • `` -> `...26`
## • `` -> `...27`ps_sputum_full <- readRDS("/Users/rebecca/Documents/Forschung/IMMProveCF/R_analysis/rds_files/ps_IMProveCF_patients_Run1-23_18102023.rds")
df <- clean_names(df)
df <- df %>%
filter(!is.na(eppi_n))
df <- df[2:54, ]
df <- df %>%
select(-c(na_cl_conc, x14)) %>% # those are meaningless
rename(na_cl_conc_mmolL_TIT=x15) %>%
rename(comments=x16) %>%
rename(na_cl_conc_mmolL_ISE=x19) %>%
rename(na_cl_conc_mmolL_COND=x23) %>%
rename(confidence_titration=x26) %>%
rename(confidence_anlytics=x27)
df <- df %>%
mutate(across(8:21, as.numeric)) %>%
mutate(visit = as_factor(gsub("V", "", sample))) %>%
mutate(visit = factor(visit, levels = sort(as.numeric(levels(factor(visit))))))## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(8:21, as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.3 all measures
df %>%
ggplot(aes(sample, na_cl_conc_mmolL_TIT))+
geom_boxplot()+
geom_point()
df %>%
ggplot(aes(sample, na_cl_conc_mmolL_ISE))+
geom_boxplot()+
geom_point()## Warning: Removed 17 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 17 rows containing missing values or values outside the scale range
## (`geom_point()`).
df %>%
ggplot(aes(sample, na_cl_conc_mmolL_COND))+
geom_boxplot()+
geom_point()## Warning: Removed 14 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 14 rows containing missing values or values outside the scale range
## (`geom_point()`).
4 only confidence + and ?
df_titr_PQ <- df %>%
filter(confidence_titration=="+" |confidence_titration=="?")
df_titr_PQ%>%
ggplot(aes(sample, na_cl_conc_mmolL_TIT))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_bw()
summary(lmerTest::lmer(na_cl_conc_mmolL_TIT ~ sample + (1|patient), data = df_titr_PQ))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: na_cl_conc_mmolL_TIT ~ sample + (1 | patient)
## Data: df_titr_PQ
##
## REML criterion at convergence: 243.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.22336 -0.67439 -0.06745 0.66520 1.74953
##
## Random effects:
## Groups Name Variance Std.Dev.
## patient (Intercept) 328.7 18.13
## Residual 1353.9 36.80
## Number of obs: 32, groups: patient, 14
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 58.00 11.29 18.19 5.136 6.7e-05 ***
## sampleV2 -49.47 25.14 20.33 -1.968 0.0629 .
## sampleV3 -27.91 29.98 20.34 -0.931 0.3628
## sampleV4 10.32 30.47 22.98 0.339 0.7380
## sampleV5 67.15 40.00 15.36 1.679 0.1134
## sampleV6 42.06 40.00 15.36 1.052 0.3092
## sampleV7 14.19 28.94 14.86 0.490 0.6310
## sampleV8 -33.76 24.84 18.43 -1.359 0.1905
## sampleV9 -12.61 21.85 16.98 -0.577 0.5713
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) smplV2 smplV3 smplV4 smplV5 smplV6 smplV7 smplV8
## sampleV2 -0.384
## sampleV3 -0.330 0.243
## sampleV4 -0.334 0.128 0.110
## sampleV5 -0.195 0.078 0.068 0.065
## sampleV6 -0.195 0.078 0.068 0.065 0.154
## sampleV7 -0.270 0.112 0.099 0.090 0.138 0.138
## sampleV8 -0.371 0.240 0.238 0.124 0.080 0.080 0.154
## sampleV9 -0.408 0.197 0.183 0.136 0.140 0.140 0.157 0.194df_analytics_PQ <- df%>%
filter(confidence_anlytics=="+" |confidence_anlytics=="?")
df_analytics_PQ %>%
ggplot(aes(sample, na_cl_conc_mmolL_ISE))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_classic()
summary(lmerTest::lmer(na_cl_conc_mmolL_ISE ~ sample + (1|patient), data = df_analytics_PQ))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: na_cl_conc_mmolL_ISE ~ sample + (1 | patient)
## Data: df_analytics_PQ
##
## REML criterion at convergence: 255.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5474 -0.3818 0.0000 0.3121 1.5250
##
## Random effects:
## Groups Name Variance Std.Dev.
## patient (Intercept) 1618.8 40.23
## Residual 764.1 27.64
## Number of obs: 33, groups: patient, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 68.540 13.056 14.437 5.250 0.000111 ***
## sampleV2 -17.215 21.461 10.911 -0.802 0.439574
## sampleV3 -16.015 25.430 10.553 -0.630 0.542236
## sampleV4 -23.599 29.786 20.093 -0.792 0.437445
## sampleV5 58.592 31.474 8.381 1.862 0.098002 .
## sampleV6 115.471 31.474 8.381 3.669 0.005833 **
## sampleV7 44.852 22.777 8.482 1.969 0.082403 .
## sampleV8 9.087 20.648 10.123 0.440 0.669122
## sampleV9 7.768 18.129 10.651 0.428 0.676828
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) smplV2 smplV3 smplV4 smplV5 smplV6 smplV7 smplV8
## sampleV2 -0.242
## sampleV3 -0.216 0.383
## sampleV4 -0.245 0.059 0.053
## sampleV5 -0.098 0.051 0.051 0.024
## sampleV6 -0.098 0.051 0.051 0.024 0.229
## sampleV7 -0.136 0.088 0.090 0.033 0.189 0.189
## sampleV8 -0.228 0.354 0.376 0.056 0.064 0.064 0.172
## sampleV9 -0.251 0.200 0.207 0.062 0.166 0.166 0.161 0.204df_analytics_PQ %>%
ggplot(aes(sample, na_cl_conc_mmolL_COND))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_classic()
summary(lmerTest::lmer(na_cl_conc_mmolL_COND~ sample + (1|patient), data = df_analytics_PQ))## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: na_cl_conc_mmolL_COND ~ sample + (1 | patient)
## Data: df_analytics_PQ
##
## REML criterion at convergence: 257.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.34176 -0.32617 -0.04848 0.32009 2.05709
##
## Random effects:
## Groups Name Variance Std.Dev.
## patient (Intercept) 824.2 28.71
## Residual 1281.3 35.79
## Number of obs: 33, groups: patient, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 87.337 12.317 16.076 7.091 2.49e-06 ***
## sampleV2 -31.971 25.810 15.356 -1.239 0.234
## sampleV3 -25.899 30.757 14.776 -0.842 0.413
## sampleV4 -25.262 32.551 23.641 -0.776 0.445
## sampleV5 56.179 39.866 9.780 1.409 0.190
## sampleV6 87.273 39.866 9.780 2.189 0.054 .
## sampleV7 40.877 28.796 9.863 1.420 0.187
## sampleV8 3.715 25.218 13.320 0.147 0.885
## sampleV9 4.520 22.054 13.066 0.205 0.841
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) smplV2 smplV3 smplV4 smplV5 smplV6 smplV7 smplV8
## sampleV2 -0.328
## sampleV3 -0.286 0.305
## sampleV4 -0.298 0.098 0.085
## sampleV5 -0.147 0.059 0.055 0.044
## sampleV6 -0.147 0.059 0.055 0.044 0.194
## sampleV7 -0.205 0.091 0.086 0.061 0.163 0.163
## sampleV8 -0.311 0.286 0.299 0.093 0.066 0.066 0.158
## sampleV9 -0.341 0.191 0.188 0.102 0.149 0.149 0.152 0.1905 only confidence +
df_titr_P <- df %>%
filter(confidence_titration=="+")
df_titr_P %>%
ggplot(aes(sample, na_cl_conc_mmolL_TIT))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_bw()
summary(lmerTest::lmer(na_cl_conc_mmolL_TIT~ sample + (1|patient), data = df_titr_P)) ## boundary (singular) fit: see help('isSingular')## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: na_cl_conc_mmolL_TIT ~ sample + (1 | patient)
## Data: df_titr_P
##
## REML criterion at convergence: 102.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3024 -0.6348 0.0000 0.7492 1.3402
##
## Random effects:
## Groups Name Variance Std.Dev.
## patient (Intercept) 0 0.00
## Residual 1186 34.43
## Number of obs: 17, groups: patient, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 67.655 11.477 10.000 5.895 0.000152 ***
## sampleV3 -13.749 36.295 10.000 -0.379 0.712750
## sampleV4 -3.379 26.917 10.000 -0.126 0.902596
## sampleV5 67.321 36.295 10.000 1.855 0.093302 .
## sampleV6 42.234 36.295 10.000 1.164 0.271588
## sampleV7 41.676 36.295 10.000 1.148 0.277590
## sampleV9 13.356 26.917 10.000 0.496 0.630490
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) smplV3 smplV4 smplV5 smplV6 smplV7
## sampleV3 -0.316
## sampleV4 -0.426 0.135
## sampleV5 -0.316 0.100 0.135
## sampleV6 -0.316 0.100 0.135 0.100
## sampleV7 -0.316 0.100 0.135 0.100 0.100
## sampleV9 -0.426 0.135 0.182 0.135 0.135 0.135
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')df_ana_P <- df %>%
filter(confidence_anlytics=="+")
df_ana_P%>%
ggplot(aes(sample, na_cl_conc_mmolL_ISE))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_classic()
summary(lmerTest::lmer(na_cl_conc_mmolL_ISE ~ sample + (1|patient), data = df_ana_P)) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: na_cl_conc_mmolL_ISE ~ sample + (1 | patient)
## Data: df_ana_P
##
## REML criterion at convergence: 244.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.55203 -0.33136 -0.02004 0.15885 1.44578
##
## Random effects:
## Groups Name Variance Std.Dev.
## patient (Intercept) 1728.8 41.58
## Residual 717.4 26.79
## Number of obs: 32, groups: patient, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 67.9608 13.2157 14.3627 5.142 0.000138 ***
## sampleV2 -0.0378 25.6025 10.3724 -0.001 0.998850
## sampleV3 -8.0469 25.6025 10.3724 -0.314 0.759522
## sampleV4 -23.0560 29.4091 17.9571 -0.784 0.443274
## sampleV5 59.7943 30.5556 7.7321 1.957 0.087323 .
## sampleV6 116.6731 30.5556 7.7321 3.818 0.005437 **
## sampleV7 46.4910 22.1358 7.8446 2.100 0.069592 .
## sampleV8 15.0906 20.6580 9.8985 0.730 0.482018
## sampleV9 10.8495 17.7919 9.8658 0.610 0.555776
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) smplV2 smplV3 smplV4 smplV5 smplV6 smplV7 smplV8
## sampleV2 -0.209
## sampleV3 -0.209 0.453
## sampleV4 -0.238 0.050 0.050
## sampleV5 -0.094 0.055 0.055 0.022
## sampleV6 -0.094 0.055 0.055 0.022 0.232
## sampleV7 -0.131 0.099 0.099 0.031 0.191 0.191
## sampleV8 -0.220 0.418 0.418 0.052 0.068 0.068 0.179
## sampleV9 -0.243 0.230 0.230 0.058 0.169 0.169 0.166 0.225df_ana_P %>%
filter(confidence_anlytics=="+") %>%
ggplot(aes(sample, na_cl_conc_mmolL_COND))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_classic()
summary(lmerTest::lmer(na_cl_conc_mmolL_COND ~ sample + (1|patient), data = df_ana_P)) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: na_cl_conc_mmolL_COND ~ sample + (1 | patient)
## Data: df_ana_P
##
## REML criterion at convergence: 246.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.33021 -0.35838 -0.01081 0.23264 2.10295
##
## Random effects:
## Groups Name Variance Std.Dev.
## patient (Intercept) 783.2 27.99
## Residual 1288.0 35.89
## Number of obs: 32, groups: patient, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 86.965 12.217 14.956 7.119 3.57e-06 ***
## sampleV2 -12.810 31.129 15.292 -0.411 0.6864
## sampleV3 -20.468 31.129 15.292 -0.658 0.5206
## sampleV4 -24.991 32.412 22.508 -0.771 0.4487
## sampleV5 56.466 39.921 8.453 1.414 0.1930
## sampleV6 87.560 39.921 8.453 2.193 0.0578 .
## sampleV7 41.367 28.839 8.546 1.434 0.1870
## sampleV8 7.567 25.465 13.309 0.297 0.7709
## sampleV9 5.842 22.098 11.801 0.264 0.7960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) smplV2 smplV3 smplV4 smplV5 smplV6 smplV7 smplV8
## sampleV2 -0.290
## sampleV3 -0.290 0.335
## sampleV4 -0.300 0.087 0.087
## sampleV5 -0.150 0.056 0.056 0.045
## sampleV6 -0.150 0.056 0.056 0.045 0.192
## sampleV7 -0.208 0.089 0.089 0.063 0.162 0.162
## sampleV8 -0.315 0.311 0.311 0.095 0.068 0.068 0.159
## sampleV9 -0.345 0.195 0.195 0.104 0.148 0.148 0.153 0.197df_titr_PQ %>%
ggplot(aes(as.numeric(visit), na_cl_conc_mmolL_TIT))+
geom_point()+
geom_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
df_analytics_PQ %>%
ggplot(aes(as.numeric(visit), na_cl_conc_mmolL_ISE))+
geom_point()+
geom_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
df_analytics_PQ %>%
ggplot(aes(as.numeric(visit), na_cl_conc_mmolL_COND))+
geom_point()+
geom_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
6 only confidence + and ? by patient
df %>%
filter(confidence_titration=="+" |confidence_titration=="?") %>%
ggplot(aes(sample, na_cl_conc_mmolL_TIT))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_bw()+
facet_wrap(~patient)## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
df %>%
filter(confidence_anlytics=="+" |confidence_anlytics=="?") %>%
ggplot(aes(sample, na_cl_conc_mmolL_ISE))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_bw()+
facet_wrap(~patient)## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
df %>%
filter(confidence_anlytics=="+" |confidence_anlytics=="?") %>%
ggplot(aes(sample, na_cl_conc_mmolL_COND))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_bw()+
facet_wrap(~patient)## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
# only confidence + and by patient
df %>%
filter(confidence_titration=="+") %>%
ggplot(aes(sample, na_cl_conc_mmolL_TIT))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_bw()+
facet_wrap(~patient)## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
df %>%
filter(confidence_anlytics=="+") %>%
ggplot(aes(sample, na_cl_conc_mmolL_ISE))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_bw()+
facet_wrap(~patient)## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
df %>%
filter(confidence_anlytics=="+") %>%
ggplot(aes(sample, na_cl_conc_mmolL_COND))+
geom_boxplot()+
geom_point(aes(color = patient))+
scale_color_manual(values = id_palette)+
geom_line(aes(group = patient), color="grey")+
theme_bw()+
facet_wrap(~patient)## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?
7 test correlation of methods
df_fil <- df %>%
filter(!is.na(confidence_anlytics)) %>%
filter(!is.na(confidence_titration)) %>%
filter(confidence_anlytics!="-")
df_fil %>%
ggplot(aes(na_cl_conc_mmolL_TIT, na_cl_conc_mmolL_ISE))+
geom_point()+
geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'
df_fil %>%
ggplot(aes(na_cl_conc_mmolL_TIT, na_cl_conc_mmolL_COND))+
geom_point()+
geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'
df_fil %>%
ggplot(aes(na_cl_conc_mmolL_ISE, na_cl_conc_mmolL_COND))+
geom_point()+
geom_smooth(method = "lm")## `geom_smooth()` using formula = 'y ~ x'
8 combine NaCl measurements with microbiome and clinical data
ps_sputum_full <- subset_samples(ps_sputum_full, material=="Sputum")
ps_sputum_filtered <- tax_filter(ps_sputum_full, min_prevalence = 0.25) # has 91 taxa## Proportional min_prevalence given: 0.25 --> min 15/57 samples.ps_sputum_relab <- transform_sample_counts(ps_sputum_filtered, function(x) x/sum(x))
df_fil<- df_fil %>%
mutate(id_visit=paste("IMP", patient, sample, sep = ""))
df_fil_sel <- df_fil %>%
select(eppi_n, patient, visit, id_visit, na_cl_conc_mmolL_COND, na_cl_conc_mmolL_ISE, na_cl_conc_mmolL_TIT, confidence_titration, confidence_anlytics)
# extract Staphylococcus abundance from Sputum
sputum_df <- psmelt(ps_sputum_relab)
sputum_df <- sputum_df %>%
mutate(Abundance=Abundance*100) # transform relative abundance into percentages
staph_df <- sputum_df%>%
filter(Genus=="Staphylococcus")
df_staph <- df_fil_sel %>%
left_join(staph_df, by="id_visit")9 Staphylococcus vs chloride levels in sputum
df_staph %>%
ggplot(aes(chlorid_mmol_cl_l, na_cl_conc_mmolL_COND))+
geom_point()+
geom_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: Removed 10 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 10 rows containing missing values or values outside the scale range
## (`geom_point()`).
library(ggtext)## Warning: package 'ggtext' was built under R version 4.1.2visit_sum_palette <- c("black", "#C3BC3FFF","#6388B4FF","#BB7693FF","#55AD89FF")
x2labels <- c("0", "3", "6-12", "15-18", "21-24")
df_staph %>%
ggplot(aes(y = Abundance+ 0.01, x=chlorid_mmol_cl_l)) +
geom_point(aes(color = visit_sum), size = 4) +
geom_smooth(method = "lm", color = "black", se = T) +
scale_color_manual(values = visit_sum_palette, labels = x2labels) +
scale_y_log10(
breaks = c(0.01, 0.1, 1, 10, 100),
labels = c("0", "0.1", "1", "10", "100"), limits = c(0.005, 100)) +
xlab("Sweat chloride mmol/l") +
ylab("*Staphylococcus* (%) in Sputum") +
theme_classic() +
theme(
axis.title.y = ggtext::element_markdown(size = 12),
axis.title.x = ggtext::element_markdown(size = 12),
legend.title = element_text(size = 12),
text = element_text(size = 12),
legend.position = "bottom",
axis.text = element_text(size = 12)
) +
annotate("text", x = 90, y = 200, label = "fdr=0.015, Ds=0.57", size = 5) +
geom_vline(xintercept = 30, linetype = 'dotted', col = 'red') +
geom_vline(xintercept = 60, linetype = 'dotted', col = 'red') +
guides(color = guide_legend(title = "Months from\ntreatment start", title.position = "left", ncol = 3)) ## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 10 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Removed 10 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_smooth()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_text()`).
df_staph %>%
ggplot(aes(y = Abundance+ 0.01, x=na_cl_conc_mmolL_COND)) +
geom_point(aes(color = visit_sum), size = 4) +
geom_smooth(method = "lm", color = "black", se = T) +
scale_color_manual(values = visit_sum_palette, labels = x2labels) +
scale_y_log10(
breaks = c(0.01, 0.1, 1, 10, 100),
labels = c("0", "0.1", "1", "10", "100"), limits = c(0.005, 100)) +
xlab("Chloride mmol/l (conductivity) in Sputum") +
ylab("*Staphylococcus* (%) in Sputum") +
theme_classic() +
theme(
axis.title.y = ggtext::element_markdown(size = 12),
axis.title.x = ggtext::element_markdown(size = 12),
legend.title = element_text(size = 12),
text = element_text(size = 12),
legend.position = "bottom",
axis.text = element_text(size = 12)
) +
#annotate("text", x = 90, y = 200, label = "fdr=0.015, Ds=0.57", size = 5) +
#geom_vline(xintercept = 30, linetype = 'dotted', col = 'red') +
#geom_vline(xintercept = 60, linetype = 'dotted', col = 'red') +
guides(color = guide_legend(title = "Months from\ntreatment start", title.position = "left", ncol = 3)) ## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
df_staph %>%
ggplot(aes(y = Abundance+ 0.01, x=na_cl_conc_mmolL_ISE)) +
geom_point(aes(color = visit_sum), size = 4) +
geom_smooth(method = "lm", color = "black", se = T) +
scale_color_manual(values = visit_sum_palette, labels = x2labels) +
scale_y_log10(
breaks = c(0.01, 0.1, 1, 10, 100),
labels = c("0", "0.1", "1", "10", "100"), limits = c(0.005, 100)) +
xlab("Chloride mmol/l (ISE) in Sputum") +
ylab("*Staphylococcus* (%) in Sputum") +
theme_classic() +
theme(
axis.title.y = ggtext::element_markdown(size = 12),
axis.title.x = ggtext::element_markdown(size = 12),
legend.title = element_text(size = 12),
text = element_text(size = 12),
legend.position = "bottom",
axis.text = element_text(size = 12)
) +
#annotate("text", x = 90, y = 200, label = "fdr=0.015, Ds=0.57", size = 5) +
#geom_vline(xintercept = 30, linetype = 'dotted', col = 'red') +
#geom_vline(xintercept = 60, linetype = 'dotted', col = 'red') +
guides(color = guide_legend(title = "Months from\ntreatment start", title.position = "left", ncol = 3)) ## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
df_staph %>%
ggplot(aes(y = Abundance+ 0.01, x=na_cl_conc_mmolL_TIT)) +
geom_point(aes(color = visit_sum), size = 4) +
geom_smooth(method = "lm", color = "black", se = T) +
scale_color_manual(values = visit_sum_palette, labels = x2labels) +
scale_y_log10(
breaks = c(0.01, 0.1, 1, 10, 100),
labels = c("0", "0.1", "1", "10", "100"), limits = c(0.005, 100)) +
xlab("Chloride mmol/l (titration) in Sputum") +
ylab("*Staphylococcus* (%) in Sputum") +
theme_classic() +
theme(
axis.title.y = ggtext::element_markdown(size = 12),
axis.title.x = ggtext::element_markdown(size = 12),
legend.title = element_text(size = 12),
text = element_text(size = 12),
legend.position = "bottom",
axis.text = element_text(size = 12)
) +
#annotate("text", x = 90, y = 200, label = "fdr=0.015, Ds=0.57", size = 5) +
#geom_vline(xintercept = 30, linetype = 'dotted', col = 'red') +
#geom_vline(xintercept = 60, linetype = 'dotted', col = 'red') +
guides(color = guide_legend(title = "Months from\ntreatment start", title.position = "left", ncol = 3)) ## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 3 rows containing non-finite outside the scale range (`stat_smooth()`).
## Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).